判别式
突出
计算机科学
人工智能
模式识别(心理学)
卷积神经网络
特征提取
特征学习
目标检测
计算机视觉
作者
Gongyang Li,Zhen Bai,Zhi Liu,Xinpeng Zhang,Haibin Ling
出处
期刊:IEEE transactions on image processing
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:32: 5257-5269
被引量:14
标识
DOI:10.1109/tip.2023.3314285
摘要
Existing methods for Salient Object Detection in Optical Remote Sensing Images (ORSI-SOD) mainly adopt Convolutional Neural Networks (CNNs) as the backbone, such as VGG and ResNet.Since CNNs can only extract features within certain receptive fields, most ORSI-SOD methods generally follow the local-to-contextual paradigm.In this paper, we propose a novel Global Extraction Local Exploration Network (GeleNet) for ORSI-SOD following the global-to-local paradigm.Specifically, GeleNet first adopts a transformer backbone to generate four-level feature embeddings with global long-range dependencies.Then, GeleNet employs a Direction-aware Shuffle Weighted Spatial Attention Module (D-SWSAM) and its simplified version (SWSAM) to enhance local interactions, and a Knowledge Transfer Module (KTM) to further enhance cross-level contextual interactions.D-SWSAM comprehensively perceives the orientation information in the lowest-level features through directional convolutions to adapt to various orientations of salient objects in ORSIs, and effectively enhances the details of salient objects with an improved attention mechanism.SWSAM discards the directionaware part of D-SWSAM to focus on localizing salient objects in the highest-level features.KTM models the contextual correlation knowledge of two middle-level features of different scales based on the self-attention mechanism, and transfers the knowledge to the raw features to generate more discriminative features.Finally, a saliency predictor is used to generate the saliency map based on the outputs of the above three modules.Extensive experiments on three public datasets demonstrate that the proposed GeleNet outperforms relevant state-of-the-art methods.The code and results of our method are available at https://github.com/MathLee/GeleNet.
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